European Radiology

, Volume 30, Issue 1, pp 291–300 | Cite as

Baseline 3D-ADC outperforms 2D-ADC in predicting response to treatment in patients with colorectal liver metastases

  • Daniel Fadaei Fouladi
  • Manijeh Zarghampour
  • Pallavi Pandey
  • Ankur Pandey
  • Farnaz Najmi Varzaneh
  • Mounes Aliyari Ghasabeh
  • Pegah Khoshpouri
  • Ihab R. KamelEmail author
Magnetic Resonance



To examine the value of baseline 3D-ADC and to predict short-term response to treatment in patients with hepatic colorectal metastases (CLMs).


Liver MR images of 546 patients with CLMs (2008–2015) were reviewed retrospectively and 68 patients fulfilled inclusion criteria. Patients had received systemic chemotherapy (n = 17), hepatic trans-arterial chemoembolization or TACE (n = 34), and 90Y radioembolization (n = 17). Baseline (pre-treatment) 3D-ADC (volumetric) of metastatic lesions was calculated employing prototype software. RECIST 1.1 was used to assess short-term response to treatment. Prediction of response to treatment by baseline 3D-ADC and 2D-ADC (ROI-based) was also compared in all patients.


Partial response to treatment (minimum 30% decrease in tumor largest transverse diameter) was seen in 35.3% of patients; 41.2% with systemic chemotherapy, 32.4% with TACE, and 35.3% with 90Y radioembolization (p = 0.82). Median baseline 3D-ADC was significantly lower in responding than in nonresponding lesions. Area under the curve (AUC) of 3D-ADC was 0.90 in 90Y radioembolization patients, 0.88 in TACE patients, and 0.77 in systemic chemotherapy patients (p < 0.01). Optimal prediction was observed with the 10th percentile of ADC (1006 × 10−6 mm2/s), yielding sensitivity and specificity of 77.4% and 91.3%, respectively. 3D-ADC outperformed 2D-ADC in predicting response to treatment (AUC; 0.86 vs. 0.71; p < 0.001).


Baseline 3D-ADC is a highly specific biomarker in predicting partial short-term response to treatment in hepatic CLMs.

Key Points

Baseline 3D-ADC is a highly specific biomarker in predicting response to different treatments in hepatic CLMs.

The prediction level of baseline ADC is better for90Y radioembolization than for systemic chemotherapy/TACE in hepatic CLMs.

3D-ADC outperforms 2D-ADC in predicting short-term response to treatment in hepatic CLMs.


Liver neoplasms Colorectal neoplasms Diffusion magnetic resonance imaging RECIST 



Apparent diffusion coefficient


Area under the curve


Confidence interval


Colorectal metastases


Diffusion-weighted imaging


Health Insurance Portability and Accountability Act


Magnetic resonance imaging


Response evaluation criteria in solid tumors


Receiver operator characteristics


Region of interest


Selective internal radiation therapy


Trans-arterial chemoembolization



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Dr Ihab R. Kamel.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• performed at one institution


  1. 1.
    Nosher JL, Ahmed I, Patel AN et al (2015) Non-operative therapies for colorectal liver metastases. J Gastrointest Oncol 6:224–240PubMedPubMedCentralGoogle Scholar
  2. 2.
    Donadon M, Ribero D, Morris-Stiff G, Abdalla EK, Vauthey JN (2007) New paradigm in the management of liver-only metastases from colorectal cancer. Gastrointest Cancer Res 1:20–27PubMedPubMedCentralGoogle Scholar
  3. 3.
    Adam R, Kitano Y (2019) Multidisciplinary approach of liver metastases from colorectal cancer. Ann Gastroenterol Surg 3:50–56CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Oki E, Ando K, Nakanishi R et al (2018) Recent advances in treatment for colorectal liver metastasis. Ann Gastroenterol Surg 2:167–175CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Kanat O, Gewirtz A, Kemeny N (2012) What is the potential role of hepatic arterial infusion chemo-therapy in the current armamentorium against colorectal cancer. J Gastrointest Oncol 3:130–138PubMedPubMedCentralGoogle Scholar
  6. 6.
    Saied A, Katz SC, Espat NJ (2013) Regional hepatic therapies: an important component in the management of colorectal cancer liver metastases. Hepatobiliary Surg Nutr 2:97–107PubMedPubMedCentralGoogle Scholar
  7. 7.
    Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247CrossRefGoogle Scholar
  8. 8.
    Lambregts DM, Martens MH, Quah RC et al (2015) Whole-liver diffusion-weighted MRI histogram analysis: effect of the presence of colorectal hepatic metastases on the remaining liver parenchyma. Eur J Gastroenterol Hepatol 27:399–404CrossRefPubMedGoogle Scholar
  9. 9.
    Padhani AR, Liu G, Koh DM et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11:102–125CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Wybranski C, Zeile M, Löwenthal D et al (2011) Value of diffusion weighted MR imaging as an early surrogate parameter for evaluation of tumor response to high-dose-rate brachytherapy of colorectal liver metastases. Radiat Oncol 6:43CrossRefPubMedGoogle Scholar
  11. 11.
    Marugami N, Tanaka T, Kitano S et al (2009) Early detection of therapeutic response to hepatic arterial infusion chemotherapy of liver metastases from colorectal cancer using diffusion-weighted MR imaging. Cardiovasc Intervent Radiol 32:638–646CrossRefPubMedGoogle Scholar
  12. 12.
    Filss CP, Galldiks N, Stoffels G et al (2014) Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors. J Nucl Med 55:540–545CrossRefPubMedGoogle Scholar
  13. 13.
    Deckers F, De Foer B, Van Mieghem F et al (2014) Apparent diffusion coefficient measurements as very early predictive markers of response to chemotherapy in hepatic metastasis: a preliminary investigation of reproducibility and diagnostic value. J Magn Reson Imaging 40:448–456CrossRefPubMedGoogle Scholar
  14. 14.
    Fouladi D, Shao N, Zarghampour M, Pandey A, Pandey P, Kamel IR (2018) Radiographic assessment for liver tumors. In: Cardona K, Maithel SK (eds) Primary and metastatic liver tumors treatment strategy and evolving therapies. Springer Nature, Switzerland, pp 15–33CrossRefGoogle Scholar
  15. 15.
    Pandey A, Pandey P, Aliyari Ghasabeh M et al (2018) Unresectable intrahepatic cholangiocarcinoma: multiparametric MR imaging to predict patient survival. Radiology 288:109–117CrossRefPubMedGoogle Scholar
  16. 16.
    Kamel IR, Reyes DK, Liapi E, Bluemke DA, Geschwind JF (2007) Functional MR imaging assessment of tumor response after 90Y microsphere treatment in patients with unresectable hepatocellular carcinoma. J Vasc Interv Radiol 18:49–56CrossRefPubMedGoogle Scholar
  17. 17.
    Koh DM, Scurr E, Collins D et al (2007) Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol 188:1001–1008CrossRefPubMedGoogle Scholar
  18. 18.
    Liang HY, Huang YQ, Yang ZX, Ying-Ding, Zeng MS, Rao SX (2016) Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases. Eur Radiol 26:2009–2018CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Vossen JA, Buijs M, Geschwind JF et al (2009) Diffusion-weighted and Gd-EOB-DTPA-contrast-enhanced magnetic resonance imaging for characterization of tumor necrosis in an animal model. J Comput Assist Tomogr 33:626–630CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Deng J, Virmani S, Young J et al (2008) Diffusion-weighted PROPELLER MRI for quantitative assessment of liver tumor necrotic fraction and viable tumor volume in VX2 rabbits. J Magn Reson Imaging 27:1069–1076CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Cui Y, Zhang XP, Sun YS, Tang L, Shen L (2008) Apparent diffusion coefficient: potential imaging biomarker for prediction and early detection of response to chemotherapy in hepatic metastases. Radiology 248:894–900CrossRefPubMedGoogle Scholar
  22. 22.
    Matsushima S, Sato T, Nishiofuku H et al (2017) Equivalent cross-relaxation rate imaging and diffusion weighted imaging for early prediction of response to bevacizumab-containing treatment in colorectal liver metastases-preliminary study. Clin Imaging 41:1–6CrossRefPubMedGoogle Scholar
  23. 23.
    Dunet V, Halkic N, Prior JO et al (2017) Detection and viability of colorectal liver metastases after neoadjuvant chemotherapy: a multiparametric PET/CT-MRI study. Clin Nucl Med 42:258–263CrossRefPubMedGoogle Scholar
  24. 24.
    Anzidei M, Napoli A, Zaccagna F et al (2011) Liver metastases from colorectal cancer treated with conventional and antiangiogenetic chemotherapy: evaluation with liver computed tomography perfusion and magnetic resonance diffusion-weighted imaging. J Comput Assist Tomogr 35:690–696CrossRefPubMedGoogle Scholar
  25. 25.
    Heskamp S, Heijmen L, Gerrits D et al (2016) Response monitoring with [18F]FLT PET and diffusion-weighted MRI after cytotoxic 5-FU treatment in an experimental rat model for colorectal liver metastases. Mol Imaging Biol. CrossRefPubMedCentralGoogle Scholar
  26. 26.
    Heijmen L, ter Voert EE, Oyen WJ et al (2015) Multimodality imaging to predict response to systemic treatment in patients with advanced colorectal cancer. PLoS One 10:e0120823CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Schmeel FC, Simon B, Luetkens JA et al (2017) Prognostic value of pretreatment diffusion-weighted magnetic resonance imaging for outcome prediction of colorectal cancer liver metastases undergoing 90Y-microsphere radioembolization. J Cancer Res Clin Oncol. CrossRefPubMedGoogle Scholar
  28. 28.
    Lahrsow M, Albrecht MH, Bickford MW, Vogl TJ (2017) Predicting treatment response of colorectal cancer liver metastases to conventional Lipiodol-based transarterial chemoembolization using diffusion-weighted MR imaging: value of pretreatment apparent diffusion coefficients (ADC) and ADC changes under therapy. Cardiovasc Intervent Radiol 40:852–859CrossRefPubMedGoogle Scholar
  29. 29.
    Okuda H, Matsushima S, Sugiura H et al (2014) Equivalent cross-relaxation rate imaging positively correlates with pathological grade and cell density of adipocytic tumors. Magn Reson Imaging 32:206–210CrossRefPubMedGoogle Scholar
  30. 30.
    Zhang XY, Sun YS, Tang L, Xue WC, Zhang XP (2011) Correlation of diffusion-weighted imaging data with apoptotic and proliferation indexes in CT26 colorectal tumor homografts in balb/c mouse. J Magn Reson Imaging 33:1171–1176CrossRefPubMedGoogle Scholar
  31. 31.
    Kokabi N, Ludwig JM, Camacho JC, Xing M, Mittal PK, Kim HS (2015) Baseline and early MR apparent diffusion coefficient quantification as a predictor of response of unresectable hepatocellular carcinoma to doxorubicin drug-eluting bead chemoembolization. J Vasc Interv Radiol 26:1777–1786CrossRefPubMedGoogle Scholar
  32. 32.
    Barabasch A, Kraemer NA, Ciritsis A et al (2015) Diagnostic accuracy of diffusion-weighted magnetic resonance imaging versus positron emission tomography/computed tomography for early response assessment of liver metastases to Y90-radioembolization. Invest Radiol 50:409–415CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Thoeny HC, De Keyzer F, Boesch C, Hermans R (2004) Diffusion-weighted imaging of the parotid gland: influence of the choice of b-values on the apparent diffusion coefficient value. J Magn Reson Imaging 20:786–790CrossRefPubMedGoogle Scholar
  34. 34.
    Loupakis F, Schirripa M, Caparello C et al (2013) Histopathologic evaluation of liver metastases from colorectal cancer in patients treated with FOLFOXIRI plus bevacizumab. Br J Cancer 108:2549–2556CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Daniel Fadaei Fouladi
    • 1
  • Manijeh Zarghampour
    • 1
  • Pallavi Pandey
    • 1
  • Ankur Pandey
    • 1
  • Farnaz Najmi Varzaneh
    • 1
  • Mounes Aliyari Ghasabeh
    • 1
  • Pegah Khoshpouri
    • 1
  • Ihab R. Kamel
    • 1
    Email author
  1. 1.Russell H. Morgan Department of Radiology and Radiological SciencesJohns Hopkins University School of MedicineBaltimoreUSA

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